Watch a Robot Build Other Robots out of Spray Foam

Using foam to create a structure for modular robots is a fast and easy (but kinda messy) way to dynamically create robots for specific tasks

2 min read
Watch a Robot Build Other Robots out of Spray Foam

Robots are quite good at doing very specific tasks. Arguably, doing very specific tasks are what robots are best at. When you put a robot into an unknown situation, however, odds are you're not going to have a design that's optimized for whatever that situation ends up being. This is where modular robots come in handy, since they can reconfigure themselves on the fly to adapt their hardware to different tasks, and the Modular Robotics Lab at the University of Pennsylvania has come up with a wild new way of dynamically constructing robots based on their CKBot modules: spray foam.

The process starts with a "foam synthesizer cart" that deploys several CKBot clusters, each consisting of a trio of jointed CKBot modules. The CKBot clusters can move around by themselves, sort of, and combined with some helpful nudging from the cart, they can be put into whatever position necessary to form the joints of a robot. The overall structure of the robot is created with insulation foam that the cart sprays to connect the CKBot clusters in such a way as to create a quadruped robot, a snake robot, or whatever else you want. Watch:

Having a robot that shoots foam is good for lots more than building other robots; for example, Modlab has used it to pick up hazardous objects and to quickly deploy permanent doorstops. There's still some work to be done with foam control and autonomy, but Modlab is already thinking ahead. Way ahead:

"By carrying a selection of collapsible molds and a foam generator, a robot could form end effectors on a task-by-task basis -- for example, forming wheels for driving on land, impellers and oats for crossing water, and high aspect ratio wings for gliding across ravines. Molds could also be made of disposable material (e.g. paper) that forms part of the final structure. Even less carried overhead is possible by creating ad-hoc molds: making a groove in the ground or placing found objects next to each other."

With this kind of capability, you could (say) send a bunch of modules and foam to Mars, and then create whatever kind of robots you need once you get there. And with foam that dissolves or degrades, you could even recycle your old robots into new robots if the scope of the mission changes. Modular robots were a brilliant idea to begin with, but this foam stuff definitely has the potential to make them even more versatile.

[ UPenn Modlab ]

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Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman

This article is part of our special report on AI, “The Great AI Reckoning.

"I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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